An optimized solution combined a CNN model with UiPath workflows to efficiently extract UB claim pages from large medical records. By reducing AI resource consumption, the approach saved $32,500 annually, improved processing speed, and ensured scalability for growing record volumes while maintaining operational efficiency.
Separation of UB from Medical Records
Approach
- Leveraged UiPath’s IDP solution for initial document processing but identified inefficiencies with large medical records.
- Developed a TensorFlow CNN model to identify UB claim pages, deployed via an Azure Fast API endpoint.
- Integrated the CNN model with UiPath work flows to process only relevant pages, optimizing AI resource usage.
Delivered Solution
- AI-driven identification of UB claim pages to minimize data sent to UiPath IDP.
- Seamless Azure-hosted Fast API integration for efficient communication between the CNN model and UiPath workflows.
- Reduced AI resource consumption and improved cost-effectiveness while maintaining processing accuracy.
Value Generated
- Cost Savings: Cut AI unit consumption by 75%, saving $32,500 annually for 6,500monthly records.
- Efficiency Gains: Reduced processing time per document, allowing the team to focus on critical tasks.
- Scalability: Adapted to increasing medical record volumes and extended to future use cases requiring document segmentation.
- Operational Impact: Streamlined document processing pipeline for long-term efficiency and resource optimization.
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